Structural, Syntactic, and Statistical Pattern Recognition

1. Front Matter

   Pages I-XIII

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2. Classification and Clustering

   1. Front Matter

      Pages 1-1

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   2. Image Annotation Using a Semantic Hierarchy

      • Abdessalem Bouzaieni, Salvatore Tabbone

      Pages 3-13

   3. Malignant Brain Tumor Classification Using the Random Forest Method

      • Lichi Zhang, Han Zhang, Islem Rekik, Yaozong Gao, Qian Wang, Dinggang Shen

      Pages 14-21

   4. Rotationally Invariant Bark Recognition

      • Václav Remeš, Michal Haindl

      Pages 22-31

   5. Dynamic Voting in Multi-view Learning for Radiomics Applications

      • Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin

      Pages 32-41

   6. Iterative Deep Subspace Clustering

      • Lei Zhou, Shuai Wang, Xiao Bai, Jun Zhou, Edwin Hancock

      Pages 42-51

   7. A Scalable Spectral Clustering Algorithm Based on Landmark-Embedding and Cosine Similarity

      • Guangliang Chen

      Pages 52-62

3. Deep Learning and Neural Networks

   1. Front Matter

      Pages 63-63

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   2. On Fast Sample Preselection for Speeding up Convolutional Neural Network Training

      • Frédéric Rayar, Seiichi Uchida

      Pages 65-75

   3. UAV First View Landmark Localization via Deep Reinforcement Learning

      • Xinran Wang, Peng Ren, Leijian Yu, Lirong Han, Xiaogang Deng

      Pages 76-85

   4. Context Free Band Reduction Using a Convolutional Neural Network

      • Ran Wei, Antonio Robles-Kelly, José Álvarez

      Pages 86-96

   5. Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks

      • Évariste Daller, Sébastien Bougleux, Luc Brun, Olivier Lézoray

      Pages 97-106

   6. Learning Deep Embeddings via Margin-Based Discriminate Loss

      • Peng Sun, Wenzhong Tang, Xiao Bai

      Pages 107-115

4. Dissimilarity Representations and Gaussian Processes

   1. Front Matter

      Pages 117-117

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   2. Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning

      • Antonelli Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax

      Pages 119-129

   3. Local Binary Patterns Based on Subspace Representation of Image Patch for Face Recognition

      • Xin Zong

      Pages 130-139

   4. An Image-Based Representation for Graph Classification

      • Frédéric Rayar, Seiichi Uchida

      Pages 140-149

   5. Visual Tracking via Patch-Based Absorbing Markov Chain

      • Ziwei Xiong, Nan Zhao, Chenglong Li, Jin Tang

      Pages 150-159

   6. Gradient Descent for Gaussian Processes Variance Reduction

      • Lorenzo Bottarelli, Marco Loog

      Pages 160-169

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018.
The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods. 

• artificial intelligence
• clustering
• clustering algorithms
• graph theory
• graphic methods
• image processing
• image reconstruction
• image segmentation
• neural networks
• pattern recognition
• semantics
• Support Vector Machines (SVM)
• machine learning
• structural matching
• algorithm analysis and problem complexity
• data structures

• Beihang University, Beijing, China

  Xiao Bai

• University of York, York, United Kingdom

  Edwin R. Hancock

• IBM Research – Thomas J. Watson Research, Yorktown Heights, USA

  Tin Kam Ho

• University of York, Heslington, York, United Kingdom

  Richard C. Wilson

• University of Cagliari, Cagliari, Italy

  Battista Biggio

• Data 61 - CSIRO, Canberra, Australia

  Antonio Robles-Kelly

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